Gautam Sneha, Samuel Cyril, Gautam Alok Sagar, Kumar Sanjeev
Karunya Institute of Technology and Sciences, Coimbatore, 641114 Tamil Nadu India.
Department of Physics, HNB Garhwal University, Srinagar, Garhwal, Uttarakhand India.
Environ Dev Sustain. 2021;23(11):16632-16645. doi: 10.1007/s10668-021-01366-4. Epub 2021 Apr 3.
The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM, PM, NO and SO) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM and PM gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO, NO, PM and PM) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers.
本研究旨在突出印度地区2019冠状病毒病（COVID-19）感染与空气污染物之间的对比关系。在疫情期间，收集了2020年1月至2020年8月印度所有28个邦和中央直辖区的COVID-19数据（累计确诊病例和死亡病例）、空气污染物（PM、PM、NO和SO）以及气象数据（温度和相对湿度）。现在，为了了解空气污染物浓度、气象因素与COVID-19病例之间的关系，使用了非参数斯皮尔曼秩相关和肯德尔秩相关。COVID-19在印度地区显示出与温度（0.55 - 0.79）和湿度（0.14 - 0.52）呈良好的相关性。PM和PM与COVID-19病例呈强负相关，相关系数在0.64 - 0.98之间。同样，NO也呈强负相关，相关系数在0.64 - 0.98之间。在封锁之前，由于边界层高度较浅，污染参数浓度较高。但在封锁之后，空气质量指数（AQI）总体下降了33.67%。背景气象参数在污染物参数（SO、NO、PM和PM）以及COVID-19感染与经济方面的变化中起着关键作用。还绘制了欧洲中期天气预报中心得出的月平均风速。可以看出，2020年1月和2月空气质量变化最小，风速在1 - 2米/秒之间。2020年7月和8月风速最高。印度西部和南部地区的空气质量风速在4 - 8米/秒之间。大气气溶胶的降水/湿沉降进一步改善了印度的空气质量。根据一项研究，在所有其他气象参数中，相对湿度的影响与病例和死亡呈正相关。本研究的结果旨在为医疗保健政策制定者的国家和邦治理提供支持。
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